Information processing systems are often used to automatically classify various sub-objects of an object. For example, an image may be analyzed to classify various portions of the image as being a region of interest to a user. A portion containing a person's face may be a region of interest, whereas a portion containing background scenery may not be a region of interest. As another example, the video content of a television broadcast may be analyzed to detect the commercials. The detection of commercials in video content is particularly important because it provides high-level program segmentation so that other algorithms can be applied directly to the program content, rather than to the commercial content. For example, after commercials are detected, the commercials can be skipped when playing back a previously recorded video.
Many techniques have been proposed for detecting commercials. One technique generates signatures representing the audio of known commercials and then compares those signatures to the audio of a television broadcast. This technique, however, requires that the commercial be known in advance. Another technique is based in part on the detection of black frames that are used as separators between commercials and programs. The presence of black frames, however, may not be sufficient by itself to indicate a separation between a commercial and a program because commercials and programs may have black frames within their content. Many techniques, upon detecting a black frame, factor in other features of the scene or nearby scenes to help determine whether the scene is a commercial or a program. These features may include rate of scene changes, edge change ratios, motion vector length, frame luminance, letterbox and key frame distances, and so on.
There are, however, several difficulties with these proposed techniques for detecting commercials. One difficulty with the use of black frames for commercial detection is that the television broadcasts in many countries (e.g., Asian countries) do not use black frames to separate commercials and programs. Thus, techniques that rely primarily on black frame detection could not reliably detect commercials for such television broadcasts. Another difficulty is that the program content of many videos tends to look like commercial content, and vice versa, which makes reliable detection of commercials difficult. It would be desirable to have a technique that would more accurately detect commercials within a video.